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Found 8 papers, 6 papers with code
Text-to-Image Models for Counterfactual Explanations: a Black-Box Approach
This paper addresses the challenge of generating Counterfactual Explanations (CEs), involving the identification and modification of the fewest necessary features to alter a classifier's prediction for a given image.
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
To the best of our knowledge, this is the first approach that uses the reverse diffusion process to model full-body tracking as a conditional sequence generation task.
Adversarial Counterfactual Visual Explanations
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics.
Diffusion Models for Counterfactual Explanations
Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable.
A Hierarchical Assessment of Adversarial Severity
Adversarial Robustness is a growing field that evidences the brittleness of neural networks.
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks.
Gabor Layers Enhance Network Robustness
We revisit the benefits of merging classical vision concepts with deep learning models.
MAIN: Multi-Attention Instance Network for Video Segmentation
Instance-level video segmentation requires a solid integration of spatial and temporal information.
